Maximum Likelihood identification for Linear Dynamic Systems with finite Gaussian mixture noise distribution

Gustavo Bittner, Rafael Orellana, Rodrigo Carvajal, Juan C. Aguero

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

7 Citas (Scopus)

Resumen

This paper considers the identification of a linear dynamic system driven by a non-Gaussian noise distribution. The noise is approximated by a finite Gaussian mixture, whilst the parameters of the system and the parameters that approximate the noise distribution are simultaneously estimated using the principle of Maximum Likelihood. To this end, a global optimization algorithm is utilized to solve the resulting non-convex optimization problem. It is shown that our approach improves the accuracy of the estimates, when compared with classic estimation techniques such as the prediction error method (PEM), in terms of covariance of the estimation error, while also obtaining an approximation of the noise distribution. The benefits of the proposed technique are illustrated by numerical simulations.

Idioma originalInglés
Título de la publicación alojadaIEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2019
EditorialInstitute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)9781728131856
DOI
EstadoPublicada - nov. 2019
Publicado de forma externa
Evento2019 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2019 - Valparaiso, Chile
Duración: 13 nov. 201927 nov. 2019

Serie de la publicación

NombreIEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2019

Conferencia

Conferencia2019 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2019
País/TerritorioChile
CiudadValparaiso
Período13/11/1927/11/19

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